In scientific data visualization huge amounts of data are generated, which implies the task of analyzing these in an efficient way. This includes the reliable detection of important parts and a low expenditure of time and effort. This is especially important for the big-sized seismic volume datasets, that are required for the exploration of oil and gas deposits. Since the generated data is complex and a manual analysis is very time-intensive, a semi-automatic approach could on one hand reduce the time required for the analysis and on the other hand offer more flexibility, than a fully automatic approach. This master's thesis introduces an algorithm, which is capable of locating regions of interest in seismic volume data automatically by detecting anomalies in local histograms. Furthermore the results are visualized and a variety of tools for the exploration and interpretation of the detected regions are developed. The approach is evaluated by experiments with synthetic data and in interviews with domain experts on the basis of real-world data. Conclusively further improvements to integrate the algorithm into the seismic interpretation workflow are suggested.

For definite isolation and classification of important features in 3D multi-attribute volume data, multidimensional transfer functions are inalienable. Yet, when using multiple dimensions, the comprehension of the data and the interaction with it become a challenge. That- because neither the control of the versatile input parameters nor the visualization in a higher dimensional space are straightforward. The goal of this thesis is the implementation of a transfer function editor which supports the creation of a multidimensional transfer function. Therefore different visualization and interaction techniques, like Parallel Coordinates, are used. Furthermore it will be possible to choose and combine the used dimensions interactively and the rendered volume will be adapted to the user interaction in real time.